[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117468-en":3,"doc-seo-117468-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117468,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Machine Learning Technology in Biomedical Engineering - Special Issue Reprint","Machine Learning Technology in Biomedical Engineering presents a special-issue reprint edited by Hongqing Yu, Alaa AlZoubi, Yifan Zhao, and Hongbo Du. The volume consolidates open-access research spanning biomedical and bioengineering applications of machine learning, including semantic machine learning services, MRI-related signal analysis, computer vision robustness in fundus imaging, and supervised feature scoring with clustering. Additional contributions address graph convolutional approaches for cartilage segmentation, deep neural frameworks for pathological gait recognition, blockchain-assisted federated learning for pandemic prevention, synthetic medical data generation for diabetes prediction, and machine learning systems for diabetes nutrition education and non-invasive blood glucose measurement.","Special Issue Reprint  \nMachine Learning Technology in Biomedical Engineering  \nEdited by  \nHongqing Yu, Alaa AlZoubi, Yifan Zhao and Hongbo Du  \n[mdpi.com/journal/bioengineering](mdpi.com/journal/bioengineering)  \nMachine Learning Technology in Biomedical Engineering  \nMachine Learning Technology in Biomedical Engineering  \nEditors  \nHongqing Yu  \nAlaa AlZoubi  \nYifan Zhao  \nHongbo Du  \nBasel • Beijing • Wuhan • Barcelona • Belgrade • Novi Sad • Cluj • Manchester  \nEditors  \nHongqing Yu University of Derby  \nDerby UK  \nAlaa AlZoubi University of Derby Derby  \nUK  \nYifan Zhao Cranfield University Cranfield  \nUK  \nHongbo Du  \nUniversity of Buckingham Buckingham  \nUK  \nEditorial Office MDPI  \nSt. Alban-Anlage 66 4052 Basel, Switzerland  \nThis is a reprint of articles from the Special Issue published online in the open access journal Bioengineering (ISSN 2306-5354) (available at: [https://www.mdpi.com/journal/bioengineering/](https://www.mdpi.com/journal/bioengineering/)[ ](https://www.mdpi.com/journal/bioengineering/)[special](special issues/ZG3ISDXD72)[ ](special issues/ZG3ISDXD72)[issues/ZG3ISDXD72](special issues/ZG3ISDXD72)) .  \nFor citation purposes, cite each article independently as indicated on the article page online and as indicated below:  \nLastname, A.A.; Lastname, B.B. Article Title. Journal Name Year, Volume Number, Page Range.  \nISBN 978-3-7258-0803-8 (Hbk)  \nISBN 978-3-7258-0804-5 (PDF)  \n[doi.org/10.3390/books978-3-7258-0804-5](doi.org/10.3390/books978-3-7258-0804-5)  \n© 2024 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) license.  \nContents  \nAbout the Editors .............................................. vii  \nPreface .................................................... ix  \nHong Qing Yu, Sam O’Neill and Ali Kermanizadeh  \nAIMS: An Automatic Semantic Machine Learning Microservice Framework to Support  \nBiomedical and Bioengineering Research Reprinted from: Bioengineering 2023, 10, 1134, doi:10.3390/bioengineering10101134 ........ 1  \nKarim Bouzrara, Odette Fokapu, Ahmed Fakhfakh and Faouzi Derbel  \nSophisticated Study of Time, Frequency and Statistical Analysis for  \nGradient-Switching-Induced Potentials during MRI Reprinted from: Bioengineering 2023, 10, 1282, doi:10.3390/bioengineering10111282 ........ 19  \nKazuaki Ishihara and Koutarou Matsumoto  \nComparing the Robustness of ResNet, Swin-Transformer, and MLP-Mixer under Unique  \nDistribution Shifts in Fundus Images Reprinted from: Bioengineering 2023, 10, 1383, doi:10.3390/bioengineering10121383 ........ 35  \nSehee Wang, So Yeon Kim and Kyung-Ah Sohn  \nClearF++: Improved Supervised Feature Scoring Using Feature Clustering in Class-Wise  \nEmbedding and Reconstruction Reprinted from: Bioengineering 2023, 10, 824, doi:10.3390/bioengineering10070824 ......... 47  \nChristos Chadoulos, Dimitrios Tsaopoulos, Andreas Symeonidis, Serafeim Moustakidis and  \nJohn Theocharis  \nDense Multi-Scale Graph Convolutional Network for Knee Joint Cartilage Segmentation Reprinted from: Bioengineering 2024, 11, 278, doi:10.3390/bioengineering11030278 ......... 60  \nKooksung Jun, Keunhan Lee, Sanghyub Lee, Hwanho Lee and Mun Sang Kim  \nHybrid Deep Neural Network Framework Combining Skeleton and Gait Features for Pathological Gait Recognition Reprinted from: Bioengineering 2023, 10, 1133, doi:10.3390/bioengineering10101133 ........ 91  \nTianruo Cao, Yongqi Pan, Honghui Chen, Jianming Zheng and Tao Hu  \nPPChain: A Blockchain for Pandemic Preventionand Control Assisted by Federated Learning Reprinted from: Bioengineering 2023, 10, 965, doi:10.3390/bioengineering10080965 ......... 111  \nZarnigor Tagmatova, Akmalbek Abdusalomov, Rashid Nasimov, Nigorakhon Nasimova,  \nAli Hikmet Dogru and Young-Im Cho  \nNew Approach for Generating Synthetic Medical Data to Predict Typ","cbCaio5iHEED9UOp","https://ap.wps.com/l/cbCaio5iHEED9UOp","pdf",15684840,1,176,"English","en",105,"# About the Editors\n# Preface\n# Contents\n## AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research\n## Sophisticated Study of Time, Frequency and Statistical Analysis for Gradient-Switching-Induced Potentials during MRI\n## Comparing the Robustness of ResNet, Swin-Transformer, and MLP-Mixer under Unique Distribution Shifts in Fundus Images\n## ClearF++: Improved Supervised Feature Scoring Using Feature Clustering in Class-Wise Embedding and Reconstruction\n## Dense Multi-Scale Graph Convolutional Network for Knee Joint Cartilage Segmentation\n## Hybrid Deep Neural Network Framework Combining Skeleton and Gait Features for Pathological Gait Recognition\n## PPChain: A Blockchain for Pandemic Preventionand Control Assisted by Federated Learning\n## New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes\n## Implementing a Novel Machine Learning System for Nutrition Education in Diabetes Mellitus Nutritional Clinic\n## Implicit HbA1c Achieving 87% Accuracy within 90 Days in Non-Invasive Fasting Blood Glucose Measurements Using Photoplethysmography","[{\"question\":\"What is the purpose of this book?\",\"answer\":\"It is a reprint collection of articles from a special issue in the open-access journal Bioengineering, bringing together research focused on machine learning technologies in biomedical engineering.\"},{\"question\":\"Who edited the special issue reprint?\",\"answer\":\"The editors are Hongqing Yu, Alaa AlZoubi, Yifan Zhao, and Hongbo Du.\"},{\"question\":\"What topics are covered in the contents?\",\"answer\":\"Topics include biomedical semantic machine learning frameworks, MRI signal analysis, robustness of deep models for fundus images, feature scoring with clustering, medical image segmentation, gait recognition, blockchain-assisted federated learning, synthetic data generation for diabetes, and diabetes-related prediction and education systems.\"}]","Machine Learning Technology in Biomedical Engineering - 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